A platform for rapid characterization of metabolic disrupters in whole animals Mounting evidence suggest that chemical toxicants from the environment can disturb the internal milieu of the organism, creating chemical stress, which can have major consequences to development, behavior and energy utilization. Particular environmental toxicants disrupt metabolic homeostasis producing systemic fat accumulation and ultimately obesity. The vast number of environmental conditions presented to an organism makes it difficult to determine causality in obesogen biology as the metrics for fat accumulation in whole animals are scope limited and timelines extremely long to manifest affect. A system that accurately defines obesogenic potential of chemical compounds would greatly facilitate the detection and mechanistic description of ubiquitous and worrisome environmental pollutants. In this RFA we endeavor to screen through suspected obesogen compound libraries in a high throughput manner and make comparison to previously discovered metabolic disrupters in C. elegans. We will use our expertise in chemical screens to develop novel tools that describe the variety of obesogenic pathways. With new metrics derived from this study we can perform automated high throughput screens for dose effects, synthetic obesogen interactions and demonstrate the effect of metabolically sensitized genetic backgrounds on fat accumulation. Ultimately we will produce a rich description of obesogen effects and build a predictive methodology for evaluating future chemical toxicants predilection to affect lipid metabolism, induce metabolic syndrome and or generate insulin resistance. PUBLIC HEALTH RELEVANCE: Exposure to particular chemicals from the environment can alter an organism's normal body plan, change innate behaviors, and induce cancer. In addition increasing prevalence of toxic substances is closely associated with increasing incidences of obesity, but causal relationships are difficult to determine in people and in current model organisms. Here we develop a platform for the analysis of chemical obesogens deriving a robust and reproducible methodology for understanding induced metabolic changes. We will use this to build a predictive methodology for evaluating future chemical toxicants predilection to affect lipid metabolism, induce metabolic syndrome and or generate insulin resistance.